AI in multi-omics analysis in viral diseases

Viral diseases are a serious threat to human health worldwide, and are known to cause long-term health complications as well as epidemics and pandemics. Yet it is difficult to understand how they spread, persist, and damage the body, which requires advanced methods. Multi-omics datasets, including genomics, transcriptomics, epigenomics, proteomics and metabolomics provide a complete view of virus-host interactions. However, the integration and interpretation of these high dimensional datasets remain a major challenge. To overcome this, artificial Intelligence (AI), including machine learning (ML) and network-based approaches, has proven to be a powerful tool to analyze, integrate, and interpret multi-omics data. This chapter discusses examples from studies on SARS-CoV-2, HIV, influenza, EBV and others, where AI has been applied to multi-omics research. These studies show how AI-assisted multi-omics approaches are helping in the advancement of viral disease research by providing better understanding of virus induced cellular changes, identifying biomarkers, designing targeted therapies etc. Therefore, integrating multi-omics with AI enables improved diagnosis, treatment, and prevention of viral diseases. We have also discussed challenges faced in combining multi-omics with AI and highlighted future directions that would help in enhancing AI assisted multi-omics research.